Predicting the future trajectories of the traffic agents is a gordian
technique in autonomous driving. However, trajectory prediction suffers from
data imbalance in the prevalent datasets, and the tailed data is often more
complicated and safety-critical. In this paper, we focus on dealing with the
long-tail phenomenon in trajectory prediction. Previous methods dealing with
long-tail data did not take into account the variety of motion patterns in the
tailed data. In this paper, we put forward a future enhanced contrastive
learning framework to recognize tail trajectory patterns and form a feature
space with separate pattern clusters. Furthermore, a distribution aware hyper
predictor is brought up to better utilize the shaped feature space. Our method
is a model-agnostic framework and can be plugged into many well-known
baselines. Experimental results show that our framework outperforms the
state-of-the-art long-tail prediction method on tailed samples by 9.5% on ADE
and 8.5% on FDE, while maintaining or slightly improving the averaged
performance. Our method also surpasses many long-tail techniques on trajectory
prediction task.Comment: Accepted for publication at the IEEE/CVF Conference on Computer
Vision and Pattern Recognition 2023 (CVPR 2023